code: def prediction_assertions(
      output_dir: Path, output_format: str, output_name: str, spark
  ):
      if output_format == "table":
          sdf = spark.table(output_name)
          df = sdf.toPandas()
      else:
          file_name = f"{output_name}.{output_format}"
          (output_dir / file_name).exists()
          df = pd.read_parquet(output_dir / file_name)
      assert "prediction" in df
      assert (df.prediction == 42).all()
  
imports: import os
  import tempfile
  from pathlib import Path
  from unittest import mock
  import pandas
  import pytest
  from pyspark.sql import SparkSession
  from sklearn.datasets import load_diabetes
  import mlflow
  from mlflow.exceptions import MlflowException
  from mlflow.recipes.artifacts import RegisteredModelVersionInfo
  from mlflow.recipes.steps.predict import _INPUT_FILE_NAME, _SCORED_OUTPUT_FILE_NAME, PredictStep
  from mlflow.recipes.steps.register import _REGISTERED_MV_INFO_FILE
  from mlflow.recipes.utils import _RECIPE_CONFIG_FILE_NAME
  from mlflow.utils.file_utils import read_yaml
  from tests.recipes.helper_functions import get_random_id, train_and_log_model, train_log_and_register_model
label_desc: Use image node_id 3 for calling a global function with example usage: prediction_assertions(output_dir, output_format, output_name, spark) without return types